The present invention relates generally to event planning, and more specifically to traffic impact prediction for multiple event planning.
Large scale planned events, such as sporting events and parades, attract high volumes of both pedestrians and vehicles (e.g., buses, passenger vehicles), often resulting in significant non-recurrent congestion on local transportation networks in the vicinity of the events. The local transportation networks, including the roadways used to travel to the events, are often overloaded by the additional demand as attendees simultaneously attempt to enter or exit the event. Traditionally, planning for the management of this congestion has been performed manually by individuals, such as traffic control managers, who use their past experiences to determine how to deploy traffic control agency resources in an effort to minimize bottlenecks.
Unplanned events, such as traffic incidents, severe weather, and facility problems, may also cause significant non-recurrent congestion to roadways. Non-recurrent congestion caused by unplanned events is often due to a restriction in capacity because of damaged or disabled traffic lanes or other disabled roadway infrastructures. Similar to planned events, the management of congestion caused by unplanned events is performed manually by individuals based on their past experiences.